{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T07:46:29Z","timestamp":1791963989485,"version":"4.3.4"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T00:00:00Z","timestamp":1791763200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T00:00:00Z","timestamp":1791763200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2025M771545"],"award-info":[{"award-number":["2025M771545"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013061","name":"Jilin Provincial Scientific and Technological Development Program","doi-asserted-by":"publisher","award":["20260102002JC"],"award-info":[{"award-number":["20260102002JC"]}],"id":[{"id":"10.13039\/501100013061","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Interdiscip Sci Comput Life Sci"],"DOI":"10.1007\/s12539-026-00879-8","type":"journal-article","created":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T17:39:26Z","timestamp":1791826766000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MDCL: Multi-source Information Fusion and Collaborative Contrastive Learning for Inferring miRNA-Drug Sensitivity and Resistance Associations"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3233-3777","authenticated-orcid":false,"given":"Lan","family":"Huang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenxu","family":"Si","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunzhi","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziyun","family":"An","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiana","family":"Ding","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiyao","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0306-9009","authenticated-orcid":false,"given":"Nan","family":"Sheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4751-0708","authenticated-orcid":false,"given":"Yan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,10,12]]},"reference":[{"issue":"4","key":"879_CR1","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1016\/j.ncrna.2021.12.001","volume":"6","author":"P Mondal","year":"2021","unstructured":"Mondal P, Meeran SM (2021) microRNAs in cancer chemoresistance: the sword and the shield. Non-coding RNA Res 6(4):200\u2013210. https:\/\/doi.org\/10.1016\/j.ncrna.2021.12.001","journal-title":"Non-coding RNA Res"},{"key":"879_CR2","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1186\/s12943-024-02005-y","volume":"23","author":"H Gao","year":"2024","unstructured":"Gao H, Xi Z, Dai J et al (2024) Drug resistance mechanisms and treatment strategies mediated by ubiquitin-specific proteases (USPs) in cancers: new directions and therapeutic options. Mol Cancer 23:88. https:\/\/doi.org\/10.1186\/s12943-024-02005-y","journal-title":"Mol Cancer"},{"key":"879_CR3","doi-asserted-by":"publisher","first-page":"1374843","DOI":"10.3389\/fmolb.2024.1374843","volume":"11","author":"C Hynes","year":"2024","unstructured":"Hynes C, Kakumani PK (2024) Regulatory role of RNA-binding proteins in microRNA biogenesis. Front Mol Biosci 11:1374843. https:\/\/doi.org\/10.3389\/fmolb.2024.1374843","journal-title":"Front Mol Biosci"},{"key":"879_CR4","doi-asserted-by":"publisher","unstructured":"Dai H, Xu LY, Qian Q et al (2019) MicroRNA-222 promotes drug resistance to doxorubicin in breast cancer via regulation of miR-222\/bim pathway. Biosci Rep 39(7):BSR20190650. https:\/\/doi.org\/10.1042\/BSR20190650","DOI":"10.1042\/BSR20190650"},{"key":"879_CR5","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1007\/s10238-024-01331-1","volume":"24","author":"A Mohajeri Khorasani","year":"2024","unstructured":"Mohajeri Khorasani A, Mohammadi S, Raghibi A et al (2024) miR-17-92a-1 cluster host gene: a key regulator in colorectal cancer development and progression. Clin Exp Med 24:85. https:\/\/doi.org\/10.1007\/s10238-024-01331-1","journal-title":"Clin Exp Med"},{"issue":"2","key":"879_CR6","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1093\/abbs\/gms099","volume":"45","author":"X Zhu","year":"2013","unstructured":"Zhu X, Li Y, Shen H et al (2013) miR-137 restoration sensitizes multidrug-resistant MCF-7\/ADM cells to anticancer agents by targeting YB-1. Acta Biochim Biophys Sin 45(2):80\u201386. https:\/\/doi.org\/10.1093\/abbs\/gms099","journal-title":"Acta Biochim Biophys Sin"},{"issue":"2","key":"879_CR7","doi-asserted-by":"publisher","first-page":"242","DOI":"10.20517\/cdr.2022.113","volume":"6","author":"M Zanjirband","year":"2023","unstructured":"Zanjirband M, Rahgozar S, Aberuyi N (2023) miR-16-5p enhances sensitivity to RG7388 through targeting PPM1D expression (WIP1) in childhood acute lymphoblastic leukemia. Cancer Drug Resist 6(2):242\u2013256","journal-title":"Cancer Drug Resist"},{"key":"879_CR8","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1186\/s13045-020-00849-7","volume":"13","author":"L Li","year":"2020","unstructured":"Li L, Wu P, Wang Z et al (2020) NoncoRNA: a database of experimentally supported non-coding RNAs and drug targets in cancer. J Hematol Oncol 13:15. https:\/\/doi.org\/10.1186\/s13045-020-00849-7","journal-title":"J Hematol Oncol"},{"issue":"24","key":"879_CR9","doi-asserted-by":"publisher","first-page":"4010","DOI":"10.1093\/bioinformatics\/btx523","volume":"33","author":"E Dai","year":"2017","unstructured":"Dai E, Yang F, Wang J et al (2017) ncDR: a comprehensive resource of non-coding RNAs involved in drug resistance. Bioinformatics 33(24):4010\u20134011. https:\/\/doi.org\/10.1093\/bioinformatics\/btx523","journal-title":"Bioinformatics"},{"issue":"D1","key":"879_CR10","doi-asserted-by":"publisher","first-page":"D1393","DOI":"10.1093\/nar\/gkad1042","volume":"52","author":"X Cao","year":"2024","unstructured":"Cao X, Zhou X, Hou F et al (2024) ncRNADrug: a database for validated and predicted ncRNAs associated with drug resistance and targeted by drugs. Nucleic Acids Res 52(D1):D1393\u2013D1399. https:\/\/doi.org\/10.1093\/nar\/gkad1042","journal-title":"Nucleic Acids Res"},{"issue":"3","key":"879_CR11","doi-asserted-by":"publisher","first-page":"851","DOI":"10.1093\/bioinformatics\/btz621","volume":"36","author":"YA Huang","year":"2020","unstructured":"Huang YA, Hu P, Chan KCC et al (2020) Graph convolution for predicting associations between miRNA and drug resistance. Bioinformatics 36(3):851\u2013858. https:\/\/doi.org\/10.1093\/bioinformatics\/btz621","journal-title":"Bioinformatics"},{"key":"879_CR12","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.987609","volume":"12","author":"T Duan","year":"2022","unstructured":"Duan T, Kuang Z, Deng L (2022) SVMMDR: prediction of miRNAs-drug resistance using support vector machines based on heterogeneous network. Front Oncol 12:987609. https:\/\/doi.org\/10.3389\/fonc.2022.987609","journal-title":"Front Oncol"},{"key":"879_CR13","doi-asserted-by":"publisher","DOI":"10.3389\/fphar.2021.799108","volume":"12","author":"Y Niu","year":"2022","unstructured":"Niu Y, Song C, Gong Y et al (2022) MiRNA-drug resistance association prediction through the attentive multimodal graph convolutional network. Front Pharmacol 12:799108. https:\/\/doi.org\/10.3389\/fphar.2021.799108","journal-title":"Front Pharmacol"},{"issue":"23","key":"879_CR14","doi-asserted-by":"publisher","first-page":"5929","DOI":"10.1021\/acs.jcim.2c01060","volume":"62","author":"L Deng","year":"2022","unstructured":"Deng L, Fan Z, Xiao X et al (2022) Dual-channel heterogeneous graph neural network for predicting microRNA-mediated drug sensitivity. J Chem Inf Model 62(23):5929\u20135937. https:\/\/doi.org\/10.1021\/acs.jcim.2c01060","journal-title":"J Chem Inf Model"},{"issue":"4","key":"879_CR15","doi-asserted-by":"publisher","first-page":"bbad247","DOI":"10.1093\/bib\/bbad247","volume":"24","author":"J Wei","year":"2023","unstructured":"Wei J, Zhuo L, Zhou Z et al (2023) GCFMCL: predicting miRNA-drug sensitivity using graph collaborative filtering and multi-view contrastive learning. Brief Bioinform 24(4):bbad247. https:\/\/doi.org\/10.1093\/bib\/bbad247","journal-title":"Brief Bioinform"},{"key":"879_CR16","doi-asserted-by":"publisher","unstructured":"Zhang Z, Zhao BW, Huang YA et al (2024) GGANet: a model for the prediction of miRNA-drug resistance based on contrastive learning and global attention. In: Huang DS, Zhang Q, Guo J (eds) Advanced Intelligent Computing in Bioinformatics. ICIC 2024. Lecture Notes in Computer Science, vol 14881. Springer, Singapore, pp 263\u2013275. https:\/\/doi.org\/10.1007\/978-981-97-5689-6_23","DOI":"10.1007\/978-981-97-5689-6_23"},{"key":"879_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2025.108542","volume":"112","author":"T Liu","year":"2026","unstructured":"Liu T, Wang S, Zhang Y et al (2026) Predicting small molecule-miRNA associations using matrix enhancement and collaborative double matrix completion. Biomed Signal Process Control 112:108542. https:\/\/doi.org\/10.1016\/j.bspc.2025.108542","journal-title":"Biomed Signal Process Control"},{"key":"879_CR18","doi-asserted-by":"publisher","DOI":"10.1007\/s11704-024-3862-1","volume":"19","author":"X Zhang","year":"2025","unstructured":"Zhang X, Lei X (2025) Predicting miRNA-drug interactions via dual-channel network based on TCN and BiLSTM. Front Comput Sci 19:195905. https:\/\/doi.org\/10.1007\/s11704-024-3862-1","journal-title":"Front Comput Sci"},{"issue":"2","key":"879_CR19","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1013968","volume":"22","author":"N Sheng","year":"2026","unstructured":"Sheng N, Liu Y, Gao L et al (2026) A pre-trained language model-based cross-modal fusion framework for predicting miRNA-drug resistance and sensitivity associations. PLoS Comput Biol 22(2):e1013968. https:\/\/doi.org\/10.1371\/journal.pcbi.1013968","journal-title":"PLoS Comput Biol"},{"issue":"D1","key":"879_CR20","doi-asserted-by":"publisher","first-page":"D155","DOI":"10.1093\/nar\/gky1141","volume":"47","author":"A Kozomara","year":"2019","unstructured":"Kozomara A, Birgaoanu M, Griffiths-Jones S (2019) miRBase: from microRNA sequences to function. Nucleic Acids Res 47(D1):D155\u2013D162. https:\/\/doi.org\/10.1093\/nar\/gky1141","journal-title":"Nucleic Acids Res"},{"issue":"D1","key":"879_CR21","doi-asserted-by":"publisher","first-page":"D1265","DOI":"10.1093\/nar\/gkad976","volume":"52","author":"C Knox","year":"2024","unstructured":"Knox C, Wilson M, Klinger CM et al (2024) DrugBank 6.0: the DrugBank knowledgebase for 2024. Nucleic Acids Res 52(D1):D1265\u2013D1275. https:\/\/doi.org\/10.1093\/nar\/gkad976","journal-title":"Nucleic Acids Res"},{"issue":"10","key":"879_CR22","doi-asserted-by":"publisher","first-page":"1391","DOI":"10.1093\/oxfordjournals.molbev.a026048","volume":"16","author":"PJ Deschavanne","year":"1999","unstructured":"Deschavanne PJ, Giron A, Vilain J et al (1999) Genomic signature: characterization and classification of species assessed by chaos game representation of sequences. Mol Biol Evol 16(10):1391\u20131399. https:\/\/doi.org\/10.1093\/oxfordjournals.molbev.a026048","journal-title":"Mol Biol Evol"},{"issue":"8","key":"879_CR23","doi-asserted-by":"publisher","first-page":"2163","DOI":"10.1093\/nar\/18.8.2163","volume":"18","author":"HJ Jeffrey","year":"1990","unstructured":"Jeffrey HJ (1990) Chaos game representation of gene structure. Nucleic Acids Res 18(8):2163\u20132170. https:\/\/doi.org\/10.1093\/nar\/18.8.2163","journal-title":"Nucleic Acids Res"},{"issue":"11","key":"879_CR24","doi-asserted-by":"publisher","first-page":"5317","DOI":"10.1021\/acs.jcim.5c00038","volume":"65","author":"N Sheng","year":"2025","unstructured":"Sheng N, Liu Y, Gao L et al (2025) Deep-learning-based integration of sequence and structure information for efficiently predicting miRNA-drug associations. J Chem Inf Model 65(11):5317\u20135326. https:\/\/doi.org\/10.1021\/acs.jcim.5c00038","journal-title":"J Chem Inf Model"},{"key":"879_CR25","doi-asserted-by":"publisher","first-page":"1201934","DOI":"10.3389\/fgene.2023.1201934","volume":"14","author":"Z Niu","year":"2023","unstructured":"Niu Z, Gao X, Xia Z et al (2023) Prediction of small molecule drug-miRNA associations based on GNNs and CNNs. Front Genet 14:1201934. https:\/\/doi.org\/10.3389\/fgene.2023.1201934","journal-title":"Front Genet"},{"key":"879_CR26","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1186\/s12859-023-05460-4","volume":"24","author":"S Pan","year":"2023","unstructured":"Pan S, Xia L, Xu L et al (2023) SubMDTA: drug target affinity prediction based on substructure extraction and multi-scale features. BMC Bioinformatics 24:334. https:\/\/doi.org\/10.1186\/s12859-023-05460-4","journal-title":"BMC Bioinformatics"},{"issue":"8","key":"879_CR27","doi-asserted-by":"publisher","first-page":"1140","DOI":"10.1093\/bioinformatics\/btaa921","volume":"37","author":"T Nguyen","year":"2021","unstructured":"Nguyen T, Le H, Quinn TP et al (2021) GraphDTA: predicting drug-target binding affinity with graph neural networks. Bioinformatics 37(8):1140\u20131147. https:\/\/doi.org\/10.1093\/bioinformatics\/btaa921","journal-title":"Bioinformatics"},{"issue":"17","key":"879_CR28","doi-asserted-by":"publisher","first-page":"4247","DOI":"10.1021\/acs.jpclett.1c00867","volume":"12","author":"Z Yang","year":"2021","unstructured":"Yang Z, Zhong W, Zhao L et al (2021) ML-DTI: mutual learning mechanism for interpretable drug-target interaction prediction. J Phys Chem Lett 12(17):4247\u20134261. https:\/\/doi.org\/10.1021\/acs.jpclett.1c00867","journal-title":"J Phys Chem Lett"},{"issue":"3","key":"879_CR29","doi-asserted-by":"publisher","first-page":"1602","DOI":"10.1109\/JBHI.2024.3375025","volume":"29","author":"L Peng","year":"2025","unstructured":"Peng L, Liu X, Yang L et al (2025) BINDTI: a bi-directional intention network for drug-target interaction identification based on attention mechanisms. IEEE J Biomed Health Inform 29(3):1602\u20131612. https:\/\/doi.org\/10.1109\/JBHI.2024.3375025","journal-title":"IEEE J Biomed Health Inform"},{"issue":"1","key":"879_CR30","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1109\/JBHI.2024.3476120","volume":"29","author":"L Peng","year":"2025","unstructured":"Peng L, Bai Z, Liu L et al (2025) DTI-MvSCA: an anti-over-smoothing multi-view framework with negative sample selection for predicting drug-target interactions. IEEE J Biomed Health Inform 29(1):711\u2013723. https:\/\/doi.org\/10.1109\/JBHI.2024.3476120","journal-title":"IEEE J Biomed Health Inform"},{"issue":"1","key":"879_CR31","doi-asserted-by":"publisher","first-page":"bbad466","DOI":"10.1093\/bib\/bbad466","volume":"25","author":"L Peng","year":"2024","unstructured":"Peng L, Huang L, Su Q et al (2024) LDA-VGHB: identifying potential lncRNA-disease associations with singular value decomposition, variational graph auto-encoder and heterogeneous Newton boosting machine. Brief Bioinform 25(1):bbad466. https:\/\/doi.org\/10.1093\/bib\/bbad466","journal-title":"Brief Bioinform"},{"issue":"12","key":"879_CR32","doi-asserted-by":"publisher","first-page":"2615","DOI":"10.1039\/D2AN00171C","volume":"147","author":"XY Hu","year":"2022","unstructured":"Hu XY, Song Z, Yang ZW et al (2022) Cancer drug resistance related microRNAs: recent advances in detection methods. Analyst 147(12):2615\u20132632. https:\/\/doi.org\/10.1039\/D2AN00171C","journal-title":"Analyst"}],"container-title":["Interdisciplinary Sciences: Computational Life Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12539-026-00879-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12539-026-00879-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12539-026-00879-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T07:03:41Z","timestamp":1791961421000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12539-026-00879-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,12]]},"references-count":32,"alternative-id":["879"],"URL":"https:\/\/doi.org\/10.1007\/s12539-026-00879-8","relation":{},"ISSN":["1913-2751","1867-1462"],"issn-type":[{"value":"1913-2751","type":"print"},{"value":"1867-1462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,12]]},"assertion":[{"value":"7 April 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 August 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 August 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 October 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":1,"name":"Ethics","label":"Conflict ot interest","group":{"name":"EthicsHeading","label":"Declarations"}}]}}